DRAC: a dynamically reconfigurable active L1 cache model for hybrid prototyping of multicore embedded systems
Bibliographic record
Abstract
This paper presents a novel dynamically reconfigurable active L1 cache model for hybrid prototyping, called DRAC. The hybrid prototyping technique simulates a multicore embedded system using an emulation kernel on top of a single physical instance of a core. We extend hybrid prototyping by supporting memory hierarchy modeling with DRAC. The presented cache model is a standalone cycle accurate model that is further customized for multicore emulation. DRAC run-time configurability enables the embedded system designer to simulate and explore different multicore design options without the need for full FPGA prototyping. Our experimental results show 2.78% average error and 5.06% worst case error when DRAC is used as a standalone cache model in a single core design. We also observed 100% relative accuracy and less than 13% absolute worst case error in timing estimation when DRAC is used for hybrid prototyping of multicore designs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".